Object-model based event detection system

ABSTRACT

Example embodiments described herein therefore relate to an object-model based event detection system that comprises a plurality of sensor devices, to perform operations that include: generating sensor data at the plurality of sensor devices; accessing the sensor data generated by the plurality of sensor devices; detecting an event, or precursor to an event, based on the sensor data, wherein the detected event corresponds to an event category; accessing an object model associated with the event type in response to detecting the event, wherein the object model defines a procedure to be applied by the event detection system to the sensor data; and streaming at least a portion of a plurality of data streams generated by the plurality of sensor devices to a server system based on the procedure, wherein the server system may perform further analysis or visualization based on the portion of the plurality of data streams.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally the field ofcommunication technology and, more particularly, but not by way oflimitation, to an architecture for systems and methods for detectingevents based on sensor data collected at one or more sensor devices.

BACKGROUND

A dashcam, or event data recorder (EDR), is an onboard camera thatcontinuously records the view through a vehicle's front windscreen andsometimes the interior of the vehicle. Some EDRs also recordacceleration, deceleration, speed, steering angle, global positioningsystem (GPS) data, and throttle position information.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 is a block diagram showing an example system for exchanging data(e.g., sensor data and associated content) over a network in accordancewith some embodiments, wherein the system includes an event detectionsystem.

FIG. 2 is a block diagram illustrating various modules of an eventdetection system, according to certain example embodiments.

FIG. 3 is a flowchart depicting a method of detecting an event based onsensor data from a plurality of sensor devices and an object model,according to certain example embodiments.

FIG. 4 is a flowchart depicting a method of accessing sensor data from aplurality of sensor devices responsive to detecting an event, accordingto certain example embodiments

FIG. 5 is a flowchart depicting a method of detecting an event,according to certain example embodiments.

FIG. 6 is a flowchart depicting a method of detecting an event,according to certain example embodiments.

FIG. 7 is an interaction diagram depicting a flow of data, according tocertain example embodiments.

FIG. 8 is an interface diagram depicting a graphical user interface topresent sensor data from one or more sensor devices, according tocertain example embodiments.

FIG. 9 is an interface diagram depicting sensor data from one or moresensor devices, according to certain example embodiments.

FIG. 10 is an interface diagram depicting sensor data from one or moresensor devices, according to certain example embodiments.

FIG. 11 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

As discussed above, EDRs may include devices with an integrated onboardcamera that continuously record data that include video, as well asacceleration, deceleration, speed, steering angle, global positioningsystem (GPS) data, and throttle position information. EDRs thereforegenerate data which can be used to review activities performed by afleet of vehicles and can be used to formulate bases for improvements insafety in a variety of areas.

While these systems are effective in recording data related to events,the existing systems lack functionality to efficiently detect andmonitor events, and precursors to events, in real-time, let alone toprovide interfaces to enable users to manage and view recorded data. Asystem which detects events and provides real-time data monitoring istherefore described below.

Example embodiments described herein relate to an event detection systemthat comprises a plurality of sensor devices to perform operations thatinclude: generating sensor data at the plurality of sensor devices;accessing the sensor data generated by the plurality of sensor devices;detecting an event, or precursor to an event, based on the sensor data,wherein the detected event corresponds to an event category; accessingan object model associated with the event type in response to detectingthe event, wherein the object model defines a procedure to be applied bythe event detection system to the sensor data; and streaming at least aportion of a plurality of data streams generated by the plurality ofsensor devices to a server system based on the procedure, wherein theserver system may perform further analysis or visualization based on theportion of the plurality of data streams.

The plurality of sensors may include a front facing camera, and a cabinfacing camera (e.g., a dashcam), as well as an accelerometer, agyroscope, one or more microphones, a temperature sensor, GPS sensors,as well as an interface to couple the event detection system with avehicle electronic control unit (ECU), and all configured to generate aplurality of data streams that may be used to detect events orprecursors to events. The plurality of sensors may in some embodimentsbe integrated into a single package, while in further embodiments, oneor more of the sensors may be mounted at remote positions from oneanother based on use and need. The plurality of sensors may further becoupled with a network gateway (hereinafter “gateway”). The gatewayfacilitates sharing sensor data generated by the plurality of sensordevices from one discrete network to another, and in some embodimentsmay perform additional processing for the event detection system.

As discussed herein, an “event” may comprise a combination of conditionsdetected by the sensor devices. An administrator of the event detectionsystem may provide event definitions, wherein each event definitionincludes one or more of: an event type or identifier (e.g., roll-over,crash, speeding, rolling stop, distracted driver); a set of thresholds;and a set of conditions represented by a set of sensor data from one ormore sensor devices from among the plurality of sensor devices of theevent detection system. For example, a “rolling stop” may be defined bya set of conditions that include: the presence of a stop sign in one ormore frames of a video stream; inertial data indicating that a stop didnot occur; and GPS data indicating that a vehicle did not stop at alocation associated with the stop sign. Similarly, a “precursor” to theevent may be defined by a portion of an event definition. For example,in the example of the “rolling stop” described above, a precursor to theevent may be defined as the detection of the stop sign in one or moreframes of the video stream.

In some embodiments, each event type or identifier of an eventdefinition may be associated with corresponding object models. An objectmodel of a particular event or precursor to an event includes the eventdefinitions of the event and precursors to the events and definesprocedures or subroutines to be performed by the event detection systemin response to detecting an event or precursor to an event. For example,the procedures of an object model may define a data-flow of sensor datathrough one or more processing components of the event detection system,processing operations to be performed upon the sensor data at the one ormore processing components of the event detection system, visualizationand display instructions for the sensor data, as well as a bit rate(hereinafter “data rate”) to generate and access the sensor dataresponsive to detecting an event or precursor to an event.

The detection of events and precursors to events may be performed by oneor more processors associated with the plurality of sensor devices, oneor more processors associated with the gateway, or by one or moreprocessors associated with the server system, or any combinationthereof.

According to certain example embodiments, the detection of events basedon the sensor data may include: detecting events based on a comparisonof sensor data to one or more thresholds defined by the eventdefinitions; detecting events by detecting features within the sensor;and detecting events based on an output of a neural network (i.e., atime delayed neural network) trained to recognize features correspondingto certain events and precursors to events.

Accordingly, in some example embodiments, detection of an event by theplurality of sensors may be based upon video data generated by one ormore cameras of the event detection system. For example, a neuralnetwork or machine learned model may be trained to recognize features orsignals corresponding to certain types of objects that correspond withan event definition (e.g., signage, a stop sign, yield, childrencrossing, rail-road, etc.). In some embodiments, the signals detectedmay also include gestures performed by an occupant of a vehicle (e.g., apeace sign). Responsive to recognizing the features that correspond tothe object associated with the event definition, the event detectionsystem may access an object model associated with the correspondingevent definition. An object model defines procedures and subroutines tobe performed by the event detection system.

Similarly, detection of an event by the plurality of sensors may bebased upon a stereoscopic inference model generated by the eventdetection system based on sensor data from one or more of the sensordevices. For example, the plurality of sensors may include a dashcam,and the sensor data may include video data that comprises monocularimage data. The event detection system may generate a depth model basedon the monocular image data through a stereoscopic inference modeltrained to construct a 3-dimensional (3D) depth model based on monocularimage data. The event detection system may compare the depth modelagainst one or more threshold values to detect events.

In further embodiments, the method of detecting an event may vary basedupon the type of sensor data accessed and analyzed. In some embodimentsthe sensor data may include inertial data, audio data, or location data.Detecting of an event may therefore include detecting one or more valuesof the sensor data transgressing threshold values corresponding to eventtypes. For example, an event may be triggered based on an inertial valuetransgressing a threshold value, or in further embodiments, an event maybe triggered based on the location data generated by a sensor devicetransgressing a boundary, or reaching a destination.

The object models associated with events may further define presentationand display instructions for sensor data of the events. The presentationand display instructions may include an identification of one or moreclient devices to present a notification responsive to the detection ofan event, as well as display instructions to visualize and displaysensor data corresponding to events. For example, the notification mayinclude a display of an identifier associated with a sensor device, aswell as one or more event attributes of the detected event.

FIG. 1 is a block diagram showing an example system 100 for detectingevents based on sensor data. The system 100 includes one or more clientdevice(s) 122 that host a number of applications including a clientapplication 114.

Accordingly, each client application 114 is able to communicate andexchange data with another client application 114 and with the serverapplication 114 executed at the server system 108 via the network 106.The data exchanged between client applications 114, and between a clientapplication 114 and the server system 108, includes functions (e.g.,commands to invoke functions) as well as payload data (e.g., text,audio, video or other multimedia data).

The server system 108 provides server-side functionality via the network106 to a particular client application 114, and in some embodiments tothe sensor device(s) 102 and the system gateway 104. While certainfunctions of the system 100 are described herein as being performed byeither a client application 114, the sensor device(s) 102, the systemgateway 104, or by the server system 108, it will be appreciated thatthe location of certain functionality either within the clientapplication 114 or the server system 108 is a design choice. Forexample, it may be technically preferable to initially deploy certaintechnology and functionality within the server system 108, but to latermigrate this technology and functionality to the client application 114,or one or more processors of the sensor device(s) 102, or system gateway104, where there may be sufficient processing capacity.

The server system 108 supports various services and operations that areprovided to the client application 114. Such operations includetransmitting data to, receiving data from, and processing data generatedby the client application 114, the sensor device(s) 102, and the systemgateway 104. In some embodiments, this data includes, message content,device information, geolocation information, persistence conditions,social network information, sensor data, and live event information, asexamples. In other embodiments, other data is used. Data exchangeswithin the system 100 are invoked and controlled through functionsavailable via graphical user interfaces (GUIs) of the client application114.

Turning now specifically to the server system 108, an ApplicationProgram Interface (API) server 110 is coupled to, and provides aprogrammatic interface to, an application server 112. The applicationserver 112 is communicatively coupled to a database server 118, whichfacilitates access to a database 120 that stores data associated withdata generated by the sensor devices 102 and processed by theapplication server 112.

Dealing specifically with the API server 110, this server receives andtransmits data (e.g., sensor data, commands, and payloads) between theclient device 122 and the application server 112. Specifically, the APIserver 110 provides a set of interfaces (e.g., routines and protocols)that can be called or queried by the client application 114 in order toinvoke functionality of the application server 112. The API server 110exposes various functions supported by the application server 112,including account registration, login functionality, the transmission ofdata, via the application server 112, from a particular clientapplication 114 to another client application 114, the sending of sensordata (e.g., images, video, geolocation data, inertial data, temperaturedata, etc.) from a client application 114 to the server application 114,and for possible access by another client application 114, the settingof a collection of data, the retrieval of such collections, theretrieval of data, and the location of devices within a region.

The application server 112 hosts a number of applications andsubsystems, including a server application 114, and an event detectionsystem 124. The event detection system 124 is configured to accesssensor data generated by the one or more sensor devices 102, detectevents or precursors to events based on the sensor data, and access anobject model that corresponds with the events or precursors to events,wherein the object model defines a data-flow, according to some exampleembodiments. Further details of the event detection system 124 can befound in FIG. 2 below.

The server application 114 implements a number of data processingtechnologies and functions, particularly related to the aggregation andother processing of data (e.g., sensor data generated by the sensordevices 102). As will be described in further detail, the sensor datagenerated by the sensor devices 102 may be aggregated into collectionsassociated with a particular user account. Other processor and memoryintensive processing of data may also be performed server-side by theserver application 114, in view of the hardware requirements for suchprocessing.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with sensor data generated by the sensor devices102 and processed by the server application 114.

FIG. 2 is a block diagram illustrating components of the event detectionsystem 124 that configure the event detection system 124 to performoperations to access sensor data generated by a plurality of sensordevices, detect events or precursors to events based on the sensor data,and to access an object model that corresponds to the event or precursorto the event detected based on the sensor data, according to someexample embodiments.

The event detection system 124 is shown as including a sensor datamodule 202, an object model module 204, a communication module 206, anda presentation module 208, all configured to communicate with each other(e.g., via a bus, shared memory, or a switch). Any one or more of thesemodules may be implemented using one or more processors 210 (e.g., byconfiguring such one or more processors to perform functions describedfor that module) and hence may include one or more of the processors210.

Any one or more of the modules described may be implemented usinghardware alone (e.g., one or more of the processors 210 of a machine) ora combination of hardware and software. For example, any moduledescribed of the event detection system 124 may physically include anarrangement of one or more of the processors 210 (e.g., a subset of oramong the one or more processors of the machine) configured to performthe operations described herein for that module. As another example, anymodule of the event detection system 124 may include software, hardware,or both, that configure an arrangement of one or more processors 210(e.g., among the one or more processors of the machine) to perform theoperations described herein for that module. Accordingly, differentmodules of the event detection system 124 may include and configuredifferent arrangements of such processors 210 or a single arrangement ofsuch processors 210 at different points in time. Moreover, any two ormore modules of the event detection system 124 may be combined into asingle module, and the functions described herein for a single modulemay be subdivided among multiple modules. Furthermore, according tovarious example embodiments, modules described herein as beingimplemented within a single machine, database, or device may bedistributed across multiple machines, databases, or devices.

As discussed above, according to certain example embodiments, the eventdetection system 124 may maintain one or more object models thatcorrespond with a plurality of event definitions, where an object modelincludes the conditions associated with an event definition andcomprises a set of procedures and subroutines to be performed by theevent detection system 124 responsive to the detection of an event orprecursor to an event. In some embodiments, a portion of the objectmodels and event definition may be stored within the databases 120, atthe server system 108, while in further embodiments, a portion of theobject models and event definitions may be stored at a local memory ofthe sensor devices 102, the gateway 104, as well as the client device122.

FIG. 3 is a flowchart depicting a method 300 of detecting an event basedon sensor data from a plurality of sensor devices 102, and an objectmodel, according to certain example embodiments. Operations of themethod 300 may be performed by the modules described above with respectto FIG. 2. As shown in FIG. 3, the method 300 includes one or moreoperations 302, 304, 306, and 308.

At operation 302, sensor data generated by at least one sensor device isaccessed at a first network node. For example, the sensor data module202 accesses sensor data generated by at least one sensor device fromamong the sensor device(s) 102 at a baseline data rate. The sensordevice may include a camera, such as a dashcam, and the sensor data maycomprise a video stream.

The sensor data module 202 detects an event or precursor to an eventbased on the sensor data, wherein the event or precursor to the eventcorresponds to an event definition. The event or precursor to the eventdetected by the sensor data module 202 can for example include a featureof a video frame from video data collected from a dashcam (e.g., asensor device 102). The feature may correspond to an object depictedwithin the video.

According to certain embodiments, the sensor data module 202 may includea machine learned model or neural network trained to recognize featurescorresponding to certain types of objects (e.g., signage, a stop sign,yield, children crossing, rail-road, etc.), and to receive sensor data,and output a result that includes an identification of an object. As anillustrative example, the sensor device 102 may include a dashcam, andthe sensor data may comprise video data generated by the dashcam andcollected by the sensor data module 202 at a first data rate, and theneural network may be trained with labeled feature data to identify oneor more events based on the labeled feature data.

At operation 306, in response to the sensor data module 202 detectingthe event or precursor to the event based on the sensor data, the objectmodel module 204 accesses an object model that corresponds with theevent or precursor to the event detected based on the sensor data. Theobject model provides event definitions associated with the precursor tothe event detected by the sensor data module 202 and provides aprocedure to be performed by the event detection system 124 thatincludes a data flow of sensor data from the sensor devices 102, and aset of conditions or criteria associated with the detected event orprecursor to the event, such as threshold values.

At operation 308, based on the procedure defined by the object model, asecond network node accesses sensor data from the portion of the sensordevices. For example, the communication module 206 causes the gateway104 to access sensor data from a portion of the sensor devices 102 basedon the procedure defined by the object model. In some embodiments, atoperation 308, the communication module 206 may additionally cause thegateway 104 to stream the sensor data from the portion of the sensordevices to the server system 108 for further analysis.

In certain example embodiments, the object model associated with aprecursor to an event may identify one or more sensor devices toactivate or access responsive to the precursor to the event. Forexample, in some embodiments, the event detection system 124 may beconfigured to access and record sensor data from a “primary” sensordevice (e.g., a dashcam), and in response to detecting a precursor to anevent, accessing one or more secondary sensor devices to retrieve sensordata.

FIG. 4 is a flowchart depicting a method 400 of accessing sensor datafrom a plurality of sensor devices 102 responsive to detecting an event,according to certain example embodiments. Operations of the method 400may be performed by the modules described above with respect to FIG. 2.As shown in FIG. 4, the method 400 includes one or more operations 402,404, 406, and 408. In some embodiments, the operations of the method 400may be performed as a precursor to, or subroutine of, one or more of theoperations of the method 300 depicted in FIG. 3, such as operation 302.

According to certain example embodiments, the sensor devices 102 mayinclude one or more cameras, such as dashcams, wherein the dashcams maybe directed inside a vehicle, to capture images and videos depictedoccupants of the vehicle, as well as directed in front of and in somecases behind the vehicle.

At operation 402, the sensor data module 202 detects a feature within aframe of a video stream from one or more of the sensor devices 102. Insome embodiments, the sensor data module 202 may include a neuralnetwork or machine learned model trained to recognize certain featuresthat correspond to events and precursors to events. For example, thefeatures may depict objects such as signs (e.g., a stop sign) as well asfacial features and expressions of a driver of a vehicle.

At operation 404, in response to detecting a feature within the frame ofthe video stream from one or more of the sensor devices 102, the sensordata module 202 determines that the feature corresponds to a precursorto an event. For example, the feature may include a gaze direction of adriver of the vehicle, or a sign depicted within a frame of the videostream.

In some embodiments, the precursor to the event may correspond with anobject model that defines conditions and thresholds associated with anevent. For example, an event may include running a stop-sign or redlight, while the precursor to the event is the detection of a sign orstop light within an image frame of the video stream generated by one ormore of the sensor devices 102. Responsive to the sensor data module 202detecting the precursor to the event, the object model module 204accesses an object model that includes an event definition comprising aset of conditions and thresholds, and provides procedures andsubroutines to be performed by one or more modules of the eventdetection system 124 in response to detecting the precursor to theevent.

At operation 406, the sensor data module 202 accesses a portion of thesensor data generated by the sensor devices 102 based on the objectmodel associated with the precursor to the event. For example, theportion of the sensor data identified by the object model based on thedefinition of the event associated with the precursor may includeinertial data, GPS data, as well as vehicle ECU data. At operation 408,the sensor data module 202 detects the event based on the portion of thesensor data generated by the sensor devices 102. In some embodiments,the operations of the method 400 may be performed by one or moreprocessors of the sensor devices 102 themselves, or by one or moreprocessors associated with the gateway 104, or any combination thereof.

In certain example embodiments, the event detection system 124 may beconfigured to access and stream sensor data from the sensor devices 102at a “baseline” data rate. For example, under normal operatingconditions, in the absence of one or more event conditions, the eventdetection system 124 may be configured to cause the sensor devices 102to generate sensor data at a reduced resolution, and to cause thegateway 104 to stream segments of sensor data to the server system 108at predefined intervals based on the baseline data rate.

Accordingly, in some embodiments, the procedures defined by the objectmodels may include instructions to cause one or more processorsassociated with the sensor devices 102, the gateway 104, and the clientdevice 122 to increase the data rate and resolution in which sensor datais generated and accessed by the event detection system 124, responsiveto detecting an event or precursor to an event.

FIG. 5 is a flowchart depicting a method 500 of detecting an event basedon sensor data generated by a plurality of sensor devices 102, accordingto certain example embodiments. Operations of the method 500 may beperformed by the modules described above with respect to FIG. 2. Asshown in FIG. 5, the method 500 includes one or more operations 502,504, and 506. The method 500 may be performed as a precursor to, orsubroutine of, one or more of the operations of the methods 300 and 400,as depicted in FIGS. 3 and 4.

At operation 502, the sensor data module 202 accesses sensor data fromthe plurality of sensor devices 102 at a first data rate. For example,the event detection system 124 may be configured to access and streamdata from the plurality of sensor devices 102 at a baseline data rate.

In some embodiments, the event detection system 124 may be configured tocause the gateway 104 to stream snapshots of sensor data from the sensordevices 102 at a predefined interval or data rate. For example, at thebaseline rate, the event detection system 124 may only stream a portionof the sensor data from the sensor devices 102 through the gateway 104to the server system 108.

At operation 504, the sensor data module 202 detects an event, or aprecursor to an event, based on the sensor data, as discussed in themethods 300 and 400 of FIGS. 3 and 4. In response to detecting the eventor precursor to the event, at operation 506, the event detection system124 may cause the sensor data module 202 to access the sensor data fromthe sensor devices 102 at a second data rate. In some embodiments, thesecond data rate may be based on an event type of the event or precursorto the event detected by the sensor data module 202.

In some embodiments, operation 506 may also include causing the sensordata module 202 to stream a sensor data from a specific set of sensordevices from among the sensor devices 102. For example, in response todetecting an event based on sensor data from a first sensor device, thesensor data module 202 may access and stream sensor data from the firstsensor device, and a second and third sensor device from among aplurality of sensor devices.

FIG. 6 is a flowchart depicting a method 600 of detecting an event basedon sensor data generated by a plurality of sensor devices 102, accordingto certain example embodiments. Operations of the method 600 may beperformed by the modules described above with respect to FIG. 2. Asshown in FIG. 6, the method 600 includes one or more operations 602,604, and 606. The method 600 may be performed as a precursor to, orsubroutine of, one or more of the operations of the methods 300, 400,and 500, as depicted in FIGS. 3, 4, and 5.

At operation 602, upon accessing the sensor data from the sensor devices102, as in operation 302 of the method 300, and operation 502 of themethod 500, the sensor data module 202 extracts a set of features fromthe sensor data. For example, the sensor data may include video datagenerated by a dashcam, wherein the dashcam is configured to captureimages of occupants of a vehicle. The set of features extracted from thesensor data may therefore include facial landmarks and image featuresdepicting the occupants of the vehicle.

In response to extracting the set of features from the sensor datagenerated by the sensor devices 102, at operation 604 the sensor datamodule 202 applies the set of features to a machine learning modeltrained to identify events and precursors to events based on features.For example, the machine learning model may be trained to identify adistracted driver based on gaze tracking features. At operation 606, thesensor data module 202 identifies an event or precursor to an eventbased on the machine learned model.

FIG. 7 is an interaction diagram 700 depicting a flow of sensor datagenerated by the sensor devices 102, according to certain exampleembodiments. Operations of the interaction diagram 700 may be performedby the modules described above with respect to FIG. 2.

At operation 702, the sensor devices 102 detects a precursor to an eventbased on a first portion of sensor data generated by the sensor devices102. For example, the first portion of the sensor data may include videodata generated by a dashcam, and the precursor to the event may includea feature within the sensor data. For example, sensor devices 102 mayinclude a neural network trained to identify objects correlated withprecursors to events.

At operation 704, based on detecting the precursor to the event, thesensor devices 102 determines an event type that corresponds with theprecursor to the event. The event type identifies an object model thatdefines a data flow of the sensor data.

At operation 706, the sensor devices 102 streams a second portion of thesensor data to the system gateway 104, where the second portion of thesensor data is based on the object model associated with the event type.At operation 708, the system gateway 104 detects an event based on theprecursor to the event detected by the sensor device 102 and the secondportion of the sensor data streamed by the sensor devices 102 to thesystem gateway 104.

At operation 710, based on the system gateway 104 detecting the eventbased on the second portion of the sensor data, the sensor devices 102multiplex the sensor data from the plurality of sensors 102 and streamthe multiplexed sensor data to the server system 108. At operation 712,the system gateway 104 routes the sensor data from the plurality ofsensors 102 to the server system 108. At operation 714, the serversystem 108 receives the multiplexed sensor data.

In some embodiments, a portion of the plurality of sensors 102 may beintegrated into the system gateway 104. For example, the system gateway104 may include one or more sensor devices configured to generate sensordata. In further embodiments, the system gateway 104 may access theplurality of sensors 102 responsive to detecting an event based on aportion (e.g., the second portion) of the sensor data, as depicted inoperation 708.

At operation 716, based on receiving the multiplexed sensor data, theserver system 108 performs further analysis upon the sensor data, and insome embodiments may perform operations that include presenting anotification at a client device and causing display of a graphical userinterface that comprises a presentation of visualizations based on thesensor data generated by the sensor devices 102.

For example, in some embodiments, the object model corresponding to theevent may identify one or more client devices to be notified when anevent is triggered and may also define notification features of thenotifications to be presented. Responsive to detecting an event, theevent detection system 124 presents the notification at the one or moreclient devices identified within the corresponding object model, whereinthe notification includes a set of event attributes as defined based onthe notification features from the object model.

FIG. 8 is an interface diagram depicting a GUI 800 to present sensordata from one or more sensor devices 102, according to certain exampleembodiments. As seen in the GUI 800, the presentation of the sensor datamay include a video stream 805, depicting video data generated by adashcam, a video stream 810, depicting video data generated by a driverfacing dashcam, as well as a graph element 815, depicting sensor datacaptured by a vehicle ECU, and GPS data plotted over a period of time.In certain embodiments, the sensor data may also include an audio streamthat comprises audio data collected by one or more sensor devices (i.e.,microphones) in communication with the event detection system 124. Forexample, the audio data may be collected from a mobile device or devicewith similar audio recording capability, wherein the device may becoupled with one or more of the client device 122, the system gateway104, the sensor devices 102, or the event detection system 124. Couplingmay include wired as well as wireless connections. Accordingly, in suchembodiments, a presentation of the audio data may be displayed at aposition within the GUI 800, wherein the presentation of the audio datamay include a visualization of the audio data, such as a display of awaveform, or an audio spectrum indicator.

In some embodiments, the period of time depicted by the graph element815 may be based on temporal attributes of the video data depicted inthe video streams 805 and 810. For example, the video stream 805 mayinclude a display of a scrub bar, wherein a position of an indicator 820on the scrub bar indicates a point in time of the video stream.

In some embodiments, the sensor data depicted in the graph element 815may be scaled based on a length of the video data depicted in the videostream 805. For example, the position of the indicator 820 correspondswith a video frame of the video stream 805. As seen in the GUI 800, theposition of the indicator 820 aligns vertically with correspondingsensor data in the graph element 815.

Similarly, the video stream 810 may be presented based on the videostream 805. For example, a user may provide an input moving theindicator 820 to a position that corresponds with a timestamped videoframe of the video stream 805 along the scrub bar of the video stream805. Based on receiving the input, the event detection system 124updates the video stream 810 in the GUI 800 based on the position of theindicator 820 on the sub bar.

In some embodiments, the GUI 800 may include a display of an event logthat includes a display of events detected by the event detection system124, such as the event indicator 825. For example, based on detecting anevent, as discussed in the methods 300 and 400 of FIGS. 3 and 4, theevent detection system 124 may update the event log to include the eventindicator 825, wherein properties of the event indicator 825 are basedon attributes of the event.

In some embodiments, a user of the event detection system 124 mayprovide an input that selects an event indicator, such as the eventindicator 825, and the event detection system 124 may update the GUI 800to present the corresponding sensor data from the event. For example,updating the GUI 800 may include presenting a segment of the videostreams 805 and 810 based on temporal attributes of the event associatedwith the event indicator 825.

In further embodiments, the GUI 800 may also include a map image 830.The map image 830 comprises indications of locations corresponding tothe events from the event log. For example, the event indicator 825comprises an identifier (“A”) that is also presented at a location thatcorresponds with the event indicator 825 on the map image 830. A usermay therefore determine where the event associated with the eventindicator occurred based on the position of the identifier within themap image 830.

FIG. 9 is an interface diagram depicting sensor data 900 from one ormore sensor devices 102, according to certain example embodiments. Asseen in FIG. 9, the sensor data 900 may include the video stream 810depicted in FIG. 8 The video stream 810 includes video data from adashcam.

In some embodiments, a dashcam may be positioned to record video of adriver of a vehicle. Based on receiving the video data from the dashcam,the event detection system 124 may perform facial recognition to thevideo data to identify one or more occupants of the vehicle. Based onidentifying the one or more occupants of the vehicle based on the videodata, the event detection system 124 may present an identification 910that includes a display of an identifier of an occupant of the vehicle.

FIG. 10 is an interface diagram depicting sensor data 1000 from one ormore sensor devices 102, according to certain example embodiments. Asseen in FIG. 9, the sensor data 1000 may include the video stream 810depicted in FIG. 8. The video stream 810 includes video data from adashcam.

In some embodiments, a dashcam may be positioned to record video of adriver of a vehicle. Based on receiving the video data from the dashcam,the event detection system 124 may perform gaze detection on the videodata to determine a point of attention of an occupant of the vehicle.Based on performing the gaze detection on the video data, the eventdetection system 124 may cause display of the gaze indicator 1005 withinthe video stream 810.

FIG. 11 is a block diagram illustrating components of a machine 1100,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 11 shows a diagrammatic representation of the machine1100 in the example form of a computer system, within which instructions1110 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1100 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1110 may be used to implement modules or componentsdescribed herein. The instructions 1110 transform the general,non-programmed machine 1100 into a particular machine 1100 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1100 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1100 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1100 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a personal digitalassistant (PDA), an entertainment media system, a cellular telephone, asmart phone, a mobile device, a wearable device (e.g., a smart watch),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1110, sequentially or otherwise, that specify actions to betaken by machine 1100. Further, while only a single machine 1100 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1110 to perform any one or more of the methodologiesdiscussed herein.

The machine 1100 may include processors 1104, memory memory/storage1106, and I/O components 1118, which may be configured to communicatewith each other such as via a bus 1102. The memory/storage 1106 mayinclude a memory 1114, such as a main memory, or other memory storage,and a storage unit 1116, both accessible to the processors 1104 such asvia the bus 1102. The storage unit 1116 and memory 1114 store theinstructions 1110 embodying any one or more of the methodologies orfunctions described herein. The instructions 1110 may also reside,completely or partially, within the memory 1114, within the storage unit1116, within at least one of the processors 1104 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 1100. Accordingly, the memory 1114, thestorage unit 1116, and the memory of processors 1104 are examples ofmachine-readable media.

The I/O components 1118 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1118 that are included in a particular machine 1100 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1118 may include many other components that are not shown inFIG. 11. The I/O components 1118 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1118may include output components 1126 and input components 1128. The outputcomponents 1126 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1128 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1118 may includebiometric components 1130, motion components 1134, environmentalenvironment components 1136, or position components 1138 among a widearray of other components. For example, the biometric components 1130may include components to detect expressions (e.g., hand expressions,facial expressions, vocal expressions, body gestures, or eye tracking),measure biosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1134 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1136 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 1138 mayinclude location sensor components (e.g., a Global Position system (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1118 may include communication components 1140operable to couple the machine 1100 to a network 1132 or devices 1120via coupling 1122 and coupling 1124 respectively. For example, thecommunication components 1140 may include a network interface componentor other suitable device to interface with the network 1132. In furtherexamples, communication components 1140 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1120 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, the communication components 1140 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1140 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1140, such as, location via Internet Protocol (IP) geo-location,location via Wi-Fi® signal triangulation, location via detecting a NFCbeacon signal that may indicate a particular location, and so forth.

GLOSSARY

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine, and includes digital or analog communications signals orother intangible medium to facilitate communication of suchinstructions. Instructions may be transmitted or received over thenetwork using a transmission medium via a network interface device andusing any one of a number of well-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smart phones, tablets, ultra books, netbooks,laptops, multi-processor systems, microprocessor-based or programmableconsumer electronics, game consoles, set-top boxes, or any othercommunication device that a user may use to access a network.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or other typeof cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1xRTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard setting organizations,other long range protocols, or other data transfer technology.

“MACHINE-READABLE MEDIUM” in this context refers to a component, deviceor other tangible media able to store instructions and data temporarilyor permanently and may include, but is not be limited to, random-accessmemory (RAM), read-only memory (ROM), buffer memory, flash memory,optical media, magnetic media, cache memory, other types of storage(e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or anysuitable combination thereof. The term “machine-readable medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-readable medium” shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a “machine-readablemedium” refers to a single storage apparatus or device, as well as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. The term “machine-readable medium”excludes signals per se.

“COMPONENT” in this context refers to a device, physical entity or logichaving boundaries defined by function or subroutine calls, branchpoints, application program interfaces (APIs), or other technologiesthat provide for the partitioning or modularization of particularprocessing or control functions. Components may be combined via theirinterfaces with other components to carry out a machine process. Acomponent may be a packaged functional hardware unit designed for usewith other components and a part of a program that usually performs aparticular function of related functions. Components may constituteeither software components (e.g., code embodied on a machine-readablemedium) or hardware components. A “hardware component” is a tangibleunit capable of performing certain operations and may be configured orarranged in a certain physical manner. In various example embodiments,one or more computer systems (e.g., a standalone computer system, aclient computer system, or a server computer system) or one or morehardware components of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a Field-Programmable Gate Array (FPGA) or an ApplicationSpecific Integrated Circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component”(or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In embodiments in which multiple hardwarecomponents are configured or instantiated at different times,communications between such hardware components may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)). The performance of certain of the operations may bedistributed among the processors, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processors or processor-implemented components may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands”, “op codes”, “machine code”, etc.) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an Application SpecificIntegrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC)or any combination thereof. A processor may further be a multi-coreprocessor having two or more independent processors (sometimes referredto as “cores”) that may execute instructions contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

“TIME DELAYED NEURAL NETWORK (TDNN)” in this context, a TDNN is anartificial neural network architecture whose primary purpose is to workon sequential data. An example would be converting continuous audio intoa stream of classified phoneme labels for speech recognition.

“BI-DIRECTIONAL LONG-SHORT TERM MEMORY (BLSTM)” in this context refersto a recurrent neural network (RNN) architecture that remembers valuesover arbitrary intervals. Stored values are not modified as learningproceeds. RNNs allow forward and backward connections between neurons.BLSTM are well-suited for the classification, processing, and predictionof time series, given time lags of unknown size and duration betweenevents.

1. A system comprising: a plurality of sensor devices that generatesensor data that comprises a plurality of data streams; a memory; and atleast one hardware processor to perform operations comprising: accessinga first data stream from among the plurality of data streams, the firstdata stream generated by a first sensor device; detecting an eventprecursor based on the first data stream; accessing an object modelassociated with the event precursor in response to the detecting theevent precursor, the object model defining a data-flow that correspondswith an event associated with the event precursor, the data-flowincluding an identification of a second data stream generated by asecond sensor device from among the plurality of sensor devices;selectively accessing the second data stream from the second sensordevice based on the identification of the portion of the plurality ofdata streams by the object model associated with the event precursor;detecting an event associated with the event precursor based on at leastthe first data stream and the second data stream, the eventcorresponding to an event type; identifying a portion of the pluralityof data streams based on the data-flow that corresponds with the eventprecursor; and causing the system to stream the portion of the pluralityof data streams that corresponds with the event to a server system basedon the data-flow defined by the object model.
 2. The system of claim 1,wherein the plurality of data streams comprise one or more of: one ormore video streams that comprise video data; an audio stream thatcomprises audio data; an accelerometer stream that comprises inertialdata; and a global positioning system stream that comprises locationdata.
 3. The system of claim 2, wherein the first data stream includes avideo stream, the second data stream includes the accelerometer stream,and the detecting the event further comprises: detecting a featurewithin a frame of the video stream from among the one or more videostreams; detecting the precursor to the event based on the featurewithin the frame of the video stream; accessing inertial data from theaccelerometer stream based on a timestamp associated with the framebased on the detecting the precursor to the event based on the featurewithin the frame of the video stream; and detecting the event based onthe inertial data and the frame of the video stream.
 4. The system ofclaim 2, wherein the detecting the event comprises: detecting the eventat the at least one sensor device based on at least the video data ofthe one or more video streams; and performing a validation of the eventbased on the video data, the location data, and the inertial data. 5.The system of claim 1, wherein the first data stream is a first videostream, and: the plurality of data streams include the first videostream and a second video stream, and the detecting the event includesdetecting the event based on the first video stream, and the second datastream; and the streaming at least the portion of the plurality of datastreams to the server system based on the data-flow defined by theobject model includes streaming the second video stream to the serversystem.
 6. The system of claim 1, wherein the streaming at least theportion of the plurality of data streams to the server system based onthe data-flow defined by the object model includes: selecting theportion of the plurality of data streams to the server system based onthe data-flow defined by the object model.
 7. The system of claim 1,wherein: the accessing of the sensor data includes accessing the sensordata generated by the at least one sensor device at a first data rate;the object model associated with the event type defines a second datarate; and the streaming of the portion of the plurality of data streamsto the server system includes streaming the portion of the plurality ofdata streams at the second data rate based on the detecting the event.8. The system of claim 1, wherein the sensor data includes video data,and the detecting the event includes: extracting a set of features fromthe video data; applying the set of features from the video data to amachine learned model; and detecting the event based on the machinelearned model.
 9. The system of claim 1, wherein the sensor dataincludes location data and speed data, and the detecting the event basedon the sensor data further comprises: determining a location based onthe location data; accessing a repository that includes a speed limitassociated with the location; and performing a comparison of the speeddata and the speed limit associated with the location.
 10. The system ofclaim 9, wherein the at least one hardware processor performs operationsfurther comprising: retrieving a threshold value associated with thespeed limit; determining that the speed data transgresses the thresholdvalue; and presenting a notification at a client device based ondetermining that the speed data transgresses the threshold value, thenotification including a display of the speed data and an identifierassociated with the sensor device.
 11. A method comprising: accessing afirst data stream from among a plurality of data streams, the first datastream generated by a first sensor device; detecting an event precursorbased on the first data stream; accessing an object model associatedwith the event precursor in response to the detecting the eventprecursor, the object model defining a data-flow that corresponds withan event associated with the event precursor, the data-flow including anidentification of a second data stream generated by a second sensordevice from among a plurality of sensor devices; selectively accessingthe second data stream from the second sensor device based on theidentification of the portion of the plurality of data streams by theobject model associated with the event precursor; detecting an eventassociated with the event precursor based on at least the first datastream and the second data stream, the event corresponding to an eventtype; identifying a portion of the plurality of data streams based onthe data-flow that corresponds with the event precursor; and causing thesystem to stream the portion of the plurality of data streams thatcorresponds with the event to a server system based on the data-flowdefined by the object model.
 12. The method of claim 11, wherein theplurality of data streams comprise one or more of: one or more videostreams that comprise video data; an audio stream that comprises audiodata; an accelerometer stream that comprises inertial data; and a globalpositioning system stream that comprises location data.
 13. The methodof claim 12, wherein the first data stream includes a video stream, thesecond data stream includes the accelerometer stream, and the detectingthe event further comprises: detecting a feature within a frame of thevideo stream from among the one or more video streams; detecting theprecursor to the event based on the feature within the frame of thevideo stream; accessing inertial data from the accelerometer streambased on a timestamp associated with the frame based on the detectingthe precursor to the event based on the feature within the frame of thevideo stream; and detecting the event based on the inertial data and theframe of the video stream.
 14. The method of claim 12, wherein thedetecting the event at the includes: detecting the event at the at leastone sensor device based on at least the video data of the one or morevideo streams; and wherein the performing the validation of the eventincludes: performing the validation of the event based on the videodata, the location data, and the inertial data.
 15. The method of claim12, wherein the first data stream is a first video stream, and: theplurality of data streams include the first video stream and a secondvideo stream, and the detecting the event includes detecting the eventbased on the first video stream and the second data stream; and thestreaming at least the portion of the plurality of data streams to theserver system based on the data-flow defined by the object modelincludes streaming the second video stream to the server system.
 16. Themethod of claim 11, wherein the streaming at least the portion of theplurality of data streams to the server system based on the data-flowdefined by the object model includes: selecting the portion of theplurality of data streams to the server system based on the data-flowdefined by the object model.
 17. The method of claim 11, wherein: theaccessing of the sensor data includes accessing the sensor datagenerated by the at least one sensor device at a first data rate; theobject model associated with the event type defines a second data rate;and the streaming of the portion of the plurality of data streams to theserver system includes streaming the portion of the plurality of datastreams at the second data rate based on the detecting the event.
 18. Anon-transitory machine-readable storage medium comprising instructionsthat, when executed by one or more processors of a machine, cause themachine to perform operations comprising: accessing a first data streamfrom among a plurality of data streams, the first data stream generatedby a first sensor device; detecting an event precursor based on thefirst data stream; accessing an object model associated with the eventprecursor in response to the detecting the event precursor, the objectmodel defining a data-flow that corresponds with an event associatedwith the event precursor, the data-flow including an identification of asecond data stream generated by a second sensor device from among theplurality of sensor devices; selectively accessing the second datastream from the second sensor device based on the identification of theportion of the plurality of data streams by the object model associatedwith the event precursor; detecting an event associated with the eventprecursor based on at least the first data stream and the second datastream, the event corresponding to an event type; identifying a portionof the plurality of data streams based on the data-flow that correspondswith the event precursor; and causing the system to stream the portionof the plurality of data streams that corresponds with the event to aserver system based on the data-flow defined by the object model. 19.The non-transitory machine-readable storage medium of claim 18, whereinthe plurality of data streams comprise one or more of: one or more videostreams that comprise video data; an audio stream that comprises audiodata; an accelerometer stream that comprises inertial data; and a globalpositioning system stream that comprises location data.
 20. Thenon-transitory machine-readable storage medium of claim 19, wherein thefirst data stream includes a video stream, the second data streamincludes the accelerometer stream, and the detecting the event furthercomprises: detecting a feature within a frame of the video stream fromamong the one or more video streams; detecting the precursor to theevent based on the feature within the frame of the video stream;accessing inertial data from the accelerometer stream based on atimestamp associated with the frame based on the detecting the precursorto the event based on the feature within the frame of the video stream;and detecting the event based on the inertial data and the frame of thevideo stream.